Identification of Some Sunflower Diseases Using Deep Convolutional Neural Networks

Yıl 2024, Cilt: 12 Sayı: 1, 11 - 19, 22.07.2024

Hakkı Fırat Altınbılek Ünal Kızıl

https://doi.org/10.33202/comuagri.1387580

Öz

Among the oilseed plants cultivated in Türkiye, sunflower ranks first in terms of cultivation area and production. Therefore, short time detection of sunflower diseases will help producers to take necessary actions on time. Computer-based deep learning techniques have made it possible to predict these diseases with high accuracy. In this study, Google Collaboratory (GC), a free cloud-based Python coding environment, was used to detect 3 different sunflower diseases. A total of 760 images were obtained and examined in the 2022-2023 production seasons in İpsala district of Edirne province. A series of data pre-processing techniques were applied to the developed Convolutional Neural Network (CNN) model and 3 different sunflower disease prediction systems were created. It has been revealed that the model can classify with an accuracy of 0.90.

Anahtar Kelimeler

Image classification, Deep learning, Machine learning, Sunflower diseases

Derin Evrişimli Sinir Ağları Kullanılarak Bazı Ayçiçeği Hastalıklarının Belirlenmesi

Öz

Türkiye’de yetiştirilen yağ bitkileri arasında ayçiçeği ekim alanı ve verim açısından ilk sırada yer almaktadır. Dolayısıyla ayçiçek hastalıklarının hızlı tespiti üeticilerin bu kısa sürede önlem almalarına yarayacaktır. Bilgisayar tabanlı derin öğrenme teknikleri bu hastalıkların yüksek doğruluk ile tahmin edilebilmesini mümkün kılmıştır. Bu çalışmada 3 farklı ayçiçek hastalığının tespitinde ücretsiz bir bulut tabanlı Python kodlama ortamı olan Google Colaboratory (GC) kullanılmıştır. Toplamda 760 görüntü Edirne ili İpsala ilçesinde 2022-2023 üretim sezonlarında elde edilerek incelenmiştir. Geliştirilen Convolutional Neural Network (CNN) modeline bir dizi veri ön işleme teknikleri uygulanmış ve 3 farklı ayçiçek hastalığı tahmin sistemi yaratılmıştır. Modelin 0.90 doğrulukla sınıflandırma yapabildiği ortaya konmuştur.

Anahtar Kelimeler

Görüntü sınıflandırma, Derin öğrenme, Makine öğrenmesi, Ayçiçek hastalıkları

Kaynakça

• Altınbilek, H.F., Kızıl, Ü., 2022. Identification of some paddy rice diseases using deep convolutional neural networks. Yuzuncu Yil University Journal of Agricultural Sciences. 32(4): 705-713.
• Aslan, M., 2022. CoviDetNet: A new -19 diagnostic system based on deep features of chest x-ray. International Journal of Imaging Systems and Technology. 32(5): 1447-1463.
• Camargo, A., Smith, J.S., 2009. An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosystems Engineering. 102: 9–21.
• Dawod, R.G., Dobre, C., 2022. Automatic segmentation and classification system for foliar diseases in sunflower. Sustainability. 14: 11312.
• Deb, D., Khan, A., Dey, N., 2020. Phoma diseases: Epidemiology and control. Plant Pathology. 69: 1203–1217.
• Demir, F., Türkoglu, M., Aslan, M., Şengür, A., 2020. A new pyramidal concatenated cnn approach for environmental sound classification. Applied Acoustics. 170: 107520.
• Devaraj, A., Rathan, K., Jaahnavi, S., Indira, K., 2019. Identification of plant disease using image processing technique. International Conference on Communication and Signal Processing. 4-6 April 2019, Chennai, India.
• Ensari, T., Armah, D.C., Balsever, A.E., Dağtekin, M., 2020. Görüntü tabanlı dijital bitki fenotiplemesi için konvolüsyonel sinir ağları. European Journal of Science and Technology. (Special Issue): 338-342.
• Ghosh, P., Mondal, A.K., Chatterjee, S., Masud, M., Meshref, H., Bairagi, A.K., 2023. Recognition of sunflower diseases using hybrid deep learning and its explainability with ai. Mathematics. 11: 2241.
• Gülzar, Y., Ünal, Z., Aktaş, H., Mir, M.S., 2023. Harnessing the power of transfer learning in sunflower disease detection: A comparative study. Agriculture. 13: 1479.
• Khirade, S.D., Patil, A. B., 2015. Plant disease detection using image processing. International Conference on Computing Communication Control and Automation. 26-27 February 2015, Pune, India.
• Lagopodi, A.L., Thanassoulopoulos, C.C., 1998. Effect of a leaf spot disease caused by alternaria alternata on sunflower in Greece. Plant Dis. 82: 41–44.
• Lati, R.N., Filin, S., Elnashef, B., Eizenberg, H., 2019. 3-D image-driven morphological crop analysis: a novel method for detection of sunflower broomrape initial subsoil parasitism. Sensors. 19(7): 1569.
• Liu, B., Ding, Z., Tian, L., He, D., Li, S., Wang, H., 2020. Grape leaf disease identification using improved deep convolutional neural networks. Frontiers in Plant Science 11.
• Lu, Y., Yi, S., Zeng, N., Liu, Y., Zhang, Y., 2017. Identification of rice diseases using deep convolutional neural networks. Neurocomputing. 267: 378-384.
• Malik, A., Vaidya, G., Jagota, V., Eswaran, S., Sirohi, A., Batra, I., Rakhra, M., Asenso, E., 2022. Design and evaluation of a hybrid technique for detecting sunflower leaf disease using deep learning approach. Hindawi Journal of Food Quality. 2022: 9211700.
• Mohanty, S.P., Hughes, D.P., Salathé, M., 2016. Using deep learning for image-based plant disease detection. Frontiers in Plant Science. 7: 1419.
• Mukhtar, I., 2009. Sunflower disease and insect pests in Pakistan: a review. African Crop Science Journal. 17(2): 109 – 118.
• Patrício, D. I., Rieder, R., 2018. Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Computers and Electronics in Agriculture. 153: 69-81.
• Radovic, M., Adarkwa, O., Wang, Q., 2017. Object recognition in aerial images using convolutional neural networks. Journal of Imaging. 3(2):21.
• Sethy, P.K., Barpanda, N.K., Rath, A.K., Behera, S.K., 2020. Image processing techniques for diagnosing rice plant disease: a survey. Procedia Computer Science. 167(220): 516–530.
• Sharma, M., Kumar, C. J., Deka, A., 2022. Early diagnosis of rice plant disease using machine learning techniques. Archives of Phytopathology and Plant Protection, 55(3): 259-283.
• Singh V, Misra, A.K., 2017. Detection of plant leaf diseases using image segmentation and soft computing techniques. Information processing in Agriculture. 2017; 4(1):41-49.
• Tümen, V., Yıldırım, O., Ergen, B., 2018. Detection of driver drowsiness in driving environment using deep learning methods. Conference: The Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science. 18-19 April 2018, İstanbul, Türkiye.
• Sirohi, A., Malik, A., 2021. A hybrid model for the classification of sunflower diseases using deep learning. 2nd International Conference on Intelligent Engineering and Management (ICIEM). 28-30 April 2021, London, United Kingdom.
• Şahin, S., Kurtulbaş, E., Toprakçı, İ., Pekel, A. G., 2021. Determination of lipid oxidation in sunflower oil treated with several additives. Biomass Conversion and Biorefinery. 13: 3953-3961.
• Şeker, A., Diri, B., Balık, H.H., 2017. Derin öğrenme yöntemleri ve uygulamaları hakkında bir inceleme. Gazi Mühendislik Bilimleri Dergisi. 3(3): 47-64.
• TÜİK, 2022. Ayçiçeği üretim miktarı, ekilen alan büyüklüğü, ithalat miktarı ve değeri, ihracat miktarı ve değeri gibi yıllar bazında sayısal veriler. https://biruni.tuik.gov.tr/medas/?locale=tr, Erişim Tarihi: 06.10.2023
• Wallelign, S., Polceanu, M., Buche C., 2018. Soybean plant disease identification using convolutional neural network. In The Thirty-First International Flairs Conference. May 2018, Melbourne, United States. 146-151.
• Wicaksono G. Andryana S., 2020. Aplikasi pendeteksi penyakit pada daun tanaman apel dengan metode convolutional neural network. Journal of Information Technology and Computer Science. 5(1): 9-16.
• Yunus Khan, T.M., Atabani, A.E., Badruddin, I.A., Badarudin, A., Khayoon, M.S., Triwahyono, S., 2014. Recent scenario and technologies to utilize non-edible oils for biodiesel production. Renewable and Sustainable Energy Reviews. 37: 840-851.